Speaker
Description
The paper examines the integration of steganography and machine learning for intelligent management of hidden information in the context of future communication systems. The emphasis is on methods for increasing resilience against attacks, dynamic optimization of hiding capacity, and adaptive detection of steganographic threats. The increasing complexity of 6G architectures, which include terahertz communications, massive MIMO systems, and network-level artificial intelligence, requires new protection strategies. Steganography, as a method for concealing information in different media, offers an additional layer of security that can be dynamically adapted by machine learning algorithms. This allows for effective management of covert channels in various scenarios, including critical infrastructures, IoT networks, and holographic communications. The report examines key algorithms, architectures, and applications of these technologies, as well as potential challenges and future research directions related to resilience against quantum attacks and the need for standardization.